A feature fusion network with spatial-temporal-enhanced strategy for the motor imagery of force intensity variation by Ankai Ying, Ankai Ying, Ankai Ying, Jinwang Lv, Jinwang Lv, Jinwang Lv, Junchen Huang, Junchen Huang, Junchen Huang, Tian Wang, Peixin Si, Peixin Si, Jiyu Zhang, Guokun Zuo, Guokun Zuo, Guokun Zuo, Guokun Zuo, Jialin Xu, Jialin Xu, Jialin Xu, Jialin Xu

“…To recognize these fine-grained features, we propose a feature fusion network with a spatial-temporal-enhanced strategy and an information reconstruction (FN-SSIR) algorithm. …”
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ST-AGRNN: A Spatio-Temporal Attention-Gated Recurrent Neural Network for Traffic State Forecasting by Jian Yang, Jinhong Li, Lu Wei, Lei Gao, Fuqi Mao

“…In the proposed model, structure-based and location-based localized spatial features are obtained simultaneously by Graph Convolutional Networks (GCNs) and DeepWalk. The localized temporal features are obtained by gated recurrent unit (GRU). …”
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A Robust Multi-Modal Deep Learning-Based Fault Diagnosis Method for PV Systems by Shahabodin Afrasiabi, Sarah Allahmoradi, Mousa Afrasiabi, Xiaodong Liang, C. Y. Chung, Jamshid Aghaei

“…The proposed method combines residual convolutional neural networks (CNNs) and gated recurrent units (GRUs) to effectively extract both spatial and temporal features from raw PV data. …”
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Graph attention networks based multi-agent path finding via temporal-spatial information aggregation. by Qingling Zhang, Peng Wang, Cui Ni, Xianchang Liu

“…This paper explores a fusion approach in both temporal and spatial dimensions based on Graph Attention Networks (GAT). …”
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Multi-Time Scale Scenario Generation for Source–Load Modeling Through Temporal Generative Adversarial Networks by Liang Ma, Shigong Jiang, Yi Song, Chenyi Si, Xiaohan Li

“…However, traditional scenario generation methods struggle with high-dimensional variables and complex spatiotemporal characteristics, posing severe challenges for distribution network planning. To address these issues, this paper proposes a multi-time scale source–load scenario generation method based on temporal convolutional networks and multi-head attention mechanisms within a temporal generative adversarial network framework. …”
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An automatic approach to detect skin cancer utilizing active infrared thermography by Ricardo F. Soto, Sebastián E. Godoy

“…Notably, our method addresses a common limitation in existing approaches—manual lesion selection—by automating the process using a U-Net convolutional neural network.We validated our system by comparing U-Net's performance with expert dermatologist segmentations, achieving a 17% improvement in the Jaccard index over a semi-automatic algorithm. …”
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An Autism Spectrum Disorder Identification Method Based on 3D-CNN and Segmented Temporal Decision Network by Zhiling Liu, Ye Chen, Xinrui Dong, Jing Liu

“…This study aims to improve the ability to capture spatiotemporal dynamics of brain activity by proposing an advanced framework. (2) Methods: This study proposes an ASD recognition method that combines 3D Convolutional Neural Networks (3D-CNNs) and segmented temporal decision networks. …”
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DPD-v2: Generalised deep particle diffusometry for varied particle shapes and experimental conditions by Pranshul Sardana, Steven T. Wereley

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Energy efficiency and system complexity analysis of CNN based hybrid precoding for cell-free massive MIMO under terahertz communication by Tadele A. Abose, Yitbarek A. Mekonen, Binyam G. Assefa, Naol W. Gudeta

“…To address these issues, a convolutional neural network (CNN)-based hybrid precoding scheme is proposed for CFMM systems operating at THz frequencies. …”
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Median interacted pigeon optimization-based hyperparameter tuning of CNN for paddy leaf disease prediction by Jasmy Davies, S. Sivakumari

“…Furthermore, to extract relevant features from images of rice leaf diseases, Convolutional Neural Networks (CNNs) require efficient hyperparameter tuning. …”
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Enhancing Medicare Fraud Detection With a CNN-Transformer-XGBoost Framework and Explainable AI by Mohammad Balayet Hossain Sakil, Md Amit Hasan, Md Shahin Alam Mozumder, Md Rokibul Hasan, Shafiul Ajam Opee, M. F. Mridha, Zeyar Aung

“…The framework integrates convolutional neural networks (CNNs), transformers, and XGBoost to capture intricate patterns in claims data while maintaining interpretability through Shapley additive explanations. …”
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Automated detection of quiet eye durations in archery using electrooculography and comparative deep learning models by Fatma Söğüt, Hüseyin Yanık, Evren Değirmenci, İnci Kesilmiş, Ülkü Çömelekoğlu

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A deep learning short-term traffic flow prediction method considering spatial-temporal association by Yang ZHANG, Yue HU, Dongrong XIN

“…The short-term traffic flow prediction is too dependent on the time correlation characteristics, which due to the problems that the correlation factors of the spatial correlation characteristics are too complicated and difficult to quantify.In response to this defect, a deep learning short-term traffic flow prediction method considering spatial-temporal association was proposed.Firstly, by constructing a spatial association measurement function that simultaneously considers distance, flow similarity, and speed similarity, the spatial correlation between the target road segment and the surrounding associated road segments was quantified and predicted.Then, a convolutional neural network model with long short-term memory neurons embedded was constructed.The long short-term memory neurons were used to extract the temporal correlation characteristics between the data, and the spatial correlation metric and the convolution transmission of traffic data were used to extract the spatial correlation characteristics between the data, so as to realize the traffic flow prediction considering the spatial-temporal association.The experimental results show that the proposed method can adapt to short-term forecasting under different traffic flow characteristics such as weekdays and weekends, and the prediction accuracy is better than that of the classical methods.In weekdays and weekends, the forecast bias are 10.45% and 12.35% respectively.…”
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System Derived Spatial-Temporal CNN for High-Density fNIRS BCI by Robin Dale, Thomas D. O'sullivan, Scott Howard, Felipe Orihuela-Espina, Hamid Dehghani

“…Enabled by the HD probe design, layered topographical maps of Oxy/deOxy Haemoglobin changes are used to train a 3D convolutional neural network (CNN), enabling simultaneous extraction of spatial and temporal features. …”
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An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection by Sarah Tipper, Hany F. Atlam, Harjinder Singh Lallie

“…The integration of Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) has proven to be a promising approach for improving video deepfake detection, achieving near-perfect accuracy. …”
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Application of state of health estimation and remaining useful life prediction for lithium-ion batteries based on AT-CNN-BiLSTM by Feng-Ming Zhao, De-Xin Gao, Yuan-Ming Cheng, Qing Yang

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Inpatient Length of Stay and Mortality Prediction Utilizing Clinical Time Series Data by Junde Chen, Mason Li, Miles Milosevich, Tiffany Le, Andrew Bahsoun, Yuxin Wen

“…The proposed model is uniquely composed of a convolutional neural network (CNN) layer, three residual blocks, an LSTM unit, an FCN module, and a self-attention module. …”
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A multi-attention TCN based electricity price forecasting method considering high proportion of wind power fluctuation by LI Zikai, YANG Bo, ZHOU Zhongtang, LI Xin, CHEN Fengwei, JIAO Runhai

“…By combining attention mechanism with time convolutional network, a double-layer multi-head self-attention time convolutional network is established to explore the temporal patterns of electricity prices and the impact of external factors on electricity prices. …”
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Research on Industrial Process Fault Diagnosis Method Based on DMCA-BiGRUN by Feng Yu, Changzhou Zhang, Jihan Li

“…With the rising automation and complexity level of industrial systems, the efficiency and accuracy of fault diagnosis have become a critical challenge. The convolutional neural network (CNN) has shown some success in the fault diagnosis field. …”
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Filling the data gap between GRACE and GRACE-FO based on a two-step reconstruction method by Fengmin Hu, Beibei Yang, Zushuai Wei, Changlu Cui, Lingkui Meng

“…This study proposed a long short-term memory (LSTM) and Bayesian convolutional neural network (BCNN) combined in an LSTM-BCNN reconstruction model. …”
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